Note: I do not maintain this gist anymore, but people report that it still works. Please check the comments for any revisions or extra things you should take into consideration.
As of writing, the Debian distro for WSL (Windows Subsystem for Linux) is quite old.
You can get more up-to-date package managers, text-editors and compilers by upgrading WSL to Debian 12 (current testing).
- Root required
- Use at your own risk, preferably on a fresh installation.
Method taken from https://github.com/pimusicbox/pimusicbox
Assuming mopidy and Iris etc are already installed, we need to install mpc, so that we can stop playback on mopidy when we use spotify connect
sudo apt-get install mpc
Then we install the easiest librespot spotify connect system
| -- Best used for learning purposes. Original developer also has an ER diagram available at https://dbseminar.r61.net/node/32 | |
| --create tables | |
| BEGIN; | |
| CREATE TABLE regions | |
| ( region_id SERIAL primary key, | |
| region_name VARCHAR(25) | |
| ); | |
| CREATE TABLE countries |
| # See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to | |
| # newer versions of the distribution. | |
| deb http://us.archive.ubuntu.com/ubuntu/ bionic main restricted | |
| # deb-src http://us.archive.ubuntu.com/ubuntu/ bionic main restricted | |
| ## Major bug fix updates produced after the final release of the | |
| ## distribution. | |
| deb http://us.archive.ubuntu.com/ubuntu/ bionic-updates main restricted | |
| # deb-src http://us.archive.ubuntu.com/ubuntu/ bionic-updates main restricted |
| class AE(nn.Module): | |
| def __init__(self, **kwargs): | |
| super().__init__() | |
| self.encoder_hidden_layer = nn.Linear( | |
| in_features=kwargs["input_shape"], out_features=128 | |
| ) | |
| self.encoder_output_layer = nn.Linear( | |
| in_features=128, out_features=128 | |
| ) | |
| self.decoder_hidden_layer = nn.Linear( |
| from numpy import linalg as la | |
| def nearestPD(A): | |
| """Find the nearest positive-definite matrix to input | |
| A Python/Numpy port of John D'Errico's `nearestSPD` MATLAB code [1], which | |
| credits [2]. | |
| [1] https://www.mathworks.com/matlabcentral/fileexchange/42885-nearestspd |
| # Import the recording process | |
| from camera_record import record_video | |
| # set recording duration | |
| record_time = 20 | |
| # To run this as part of a bigger program, use mutltiprocessing to create a seperate process | |
| # for video recording using the following lines. | |
| from multiprocessing import Process | |
| # The line above can go to the top of your file and the following lines should be placed | |
| # where ever you need to record a video | |
| # Create the process thread object |
| import cv2 | |
| # Import the video capturing function | |
| from video_capture import VideoCaptureAsync | |
| import time | |
| #Specify width and height of video to be recorded | |
| vid_w = 1280 | |
| vid_h = 720 | |
| #Intiate Video Capture object | |
| capture = VideoCaptureAsync(src=0, width=vid_w, height=vid_h) |